\name{audpc}
\alias{audpc}
%- audpc.
\title{ Calculating the absolute or relative value of the AUDPC }
\description{
   Area Under Disease Progress Curve. The AUDPC measures the disease throughout a 
   period. The AUDPC is the area that is determined by the sum of trapezes under 
   the curve.
}
\usage{
audpc(evaluation, dates, type = "absolute")
}

\arguments{
  \item{evaluation}{ Table of data of the evaluations: Data frame }
  \item{dates}{ Vector of dates corresponding to each evaluation }
  \item{type}{ relative, absolute }
}
\details{
     AUDPC. For the illustration one considers three evaluations
     (14, 21 and 28 days) and percentage of damage in the plant 40, 80 and 90
     (interval between dates of evaluation 7 days).  AUDPC = 1045.
     The evaluations can be at different interval.
}
\value{

  \item{evaluation }{data frame}
  \item{dates }{a numeric vector}
  \item{type }{text}
}
\references{ Campbell, C. L., L. V. Madden. (1990): Introduction to Plant Disease Epidemiology. John Wiley & Sons, New York City. }
\author{ Felipe de Mendiburu }

\examples{
library(agricolae)
# example 1
dates<-c(14,21,28) # days
evaluation<-data.frame(E1=40,E2=80,E3=90) # percentages
plot(dates,evaluation,type="h",ylim=c(0,100),col="red",axes=FALSE)
title(cex.main=0.8,main="Absolute or Relative AUDPC\nTotal area = 100*(28-14)=1400")
lines(dates,evaluation,col="red")
text(dates,evaluation+5,evaluation)
text(18,20,"A = (21-14)*(80+40)/2")
text(25,60,"B = (28-21)*(90+80)/2")
text(25,40,"audpc = A+B = 1015")
text(24.5,33,"relative = audpc/area = 0.725")
abline(h=0)
axis(1,dates)
axis(2,seq(0,100,5),las=2)
lines(rbind(c(14,40),c(14,100)),lty=8,col="green")
lines(rbind(c(14,100),c(28,100)),lty=8,col="green")
lines(rbind(c(28,90),c(28,100)),lty=8,col="green")
# It calculates audpc absolute
absolute<-audpc(evaluation,dates,type="absolute")
print(absolute)
rm(evaluation, dates, absolute)
# example 2
data(disease)
dates<-c(1,2,3) # week
evaluation<-disease[,c(4,5,6)]
# It calculates audpc relative
index <-audpc(evaluation, dates, type = "relative")
# Correlation between the yield and audpc
correlation(disease$yield, index, method="kendall")
# example 3
data(CIC)
comas <- CIC$comas
oxapampa <- CIC$oxapampa
dcomas <- names(comas)[9:16]
days<- as.numeric(substr(dcomas,2,3))
AUDPC<- audpc(comas[,9:16],days)
relative<-audpc(comas[,9:16],days,type = "relative")
h1<-graph.freq(AUDPC,border="red",density=4,col="blue")
table.freq(h1)
h2<-graph.freq(relative,border="red",density=4,col="blue",
frequency=2, ylab="relative frequency")
}
\keyword{ manip }% at least one, from doc/KEYWORDS